NDLI: Transfer learning with reasonable boosting strategy

Content Provider	Springer Nature Link
Author	La, Lei Guo, Qiao Cao, Qimin Wang, Yongliang
Copyright Year	2012
Abstract	The lack of labeled data is a serious problem which greatly hinders the application of text classification in new domains. In this era of information explosion, dependence of labeled data in traditional classification methods becomes ineffective in emerged new domains. The ideology of transfer learning makes it possible to use labeled identical distribution data of old domains for data mining in new domains. However, previous algorithms and practical application systems did not reach the perfect state. This paper presents a novel complete method for text categorization (TC) in new domains where the labeled data are insufficient. We first present an improved weighting strategy of boosting algorithms family to ensure training data can be used more efficiently. We then introduce boosting ideology with the novel weighting strategy into transfer learning, and a novel text classification algorithm is proposed which has the ability to use labeled data of old domains for new domain classification with a high performance. After the mathematical discussion of the proposed algorithm, we finally deploy a real-world system based on it to evaluate the novel method. Experimental results demonstrate that our method is able to achieve both ideal accuracy and efficiency in TC when dealing with cross-domain problems.
Starting Page	807
Ending Page	816
Page Count	10
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	24
Issue Number	3-4
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2012-12-21
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Text categorization Transfer learning Boosting Lack of labeled data Weighting strategy Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Learning Fuzzy Rules from Data

Text categorization based on regularization extreme learning machine

Knowledge transfer across different domain data with multiple views

Multi-label incremental learning applied to web page categorization

Transferred IR pedestrian detector toward distinct scenarios adaptation

Editorial: special issue on computational intelligence for vision and robotics

Knowledge-based extreme learning machines

A Neural Network Methodology and Strategy of Quadratic Optimisation

An improved combinatorial biclustering algorithm

Transfer learning with reasonable boosting strategy

Similar Documents

Learning Fuzzy Rules from Data

Text categorization based on regularization extreme learning machine

Knowledge transfer across different domain data with multiple views

Multi-label incremental learning applied to web page categorization

Transferred IR pedestrian detector toward distinct scenarios adaptation

Editorial: special issue on computational intelligence for vision and robotics

Knowledge-based extreme learning machines

A Neural Network Methodology and Strategy of Quadratic Optimisation

An improved combinatorial biclustering algorithm

Transfer learning with reasonable boosting strategy